Unorganized neural networks applied to streamflow forecasting of Passo Real hydroelectric plant

Abstract

Unorganized neural networks — or unorganized machines - are recent developed architectures in the field of computational intelligence, in which the supervised tuning of the free parameters is restricted to the weights of the output layer, by means of a linear least square solution. The remaining weights are randomly generated and stand untrained which become the adjustment process simple and fast. In this work, we considered the Extreme Learning Machines (ELMs) and Echo State Networks (ESNs) to predict the monthly seasonal streamflow series associated to Passo Real hydroelectric plant, located in Brazil. In view of to establish a performance comparison, a periodic autoregressive model was developed. The computational results obtained show the relevance of the proposed networks to solve the problem, extending the possibility of application of the unorganized networks.